Medical image processing apparatus, medical image processing method, program, and endoscope system

    公开(公告)号:US12059123B2

    公开(公告)日:2024-08-13

    申请号:US17180638

    申请日:2021-02-19

    Inventor: Shumpei Kamon

    Abstract: The present invention aims to provide a medical image processing apparatus, a medical image processing method, a program, and an endoscope system that report a region of interest without interrupting observation of a medical image. The above problem is solved by a medical image processing apparatus including: an emphasis processing unit that emphasizes a position of a region of interest included in a plurality of medical images sequentially displayed on a display unit; and a transition time setting unit that sets a transition time in accordance with a feature quantity of the region of interest, in which the emphasis processing unit emphasizes the position of the region of interest at a first emphasis level and, after the transition time has elapsed from emphasis at the first emphasis level, emphasizes the position of the region of interest at a second emphasis level relatively lower than the first emphasis level.

    Filing device, filing method, and program

    公开(公告)号:US11704794B2

    公开(公告)日:2023-07-18

    申请号:US16941552

    申请日:2020-07-29

    Abstract: There is provided a filing device, a filing method, and a program that cause a still image and a motion image to be associated with each other, which are captured without being associated with each other.
    The problem is solved through a filing device including an acquisition unit that acquires at least one still image and at least one motion image, a still image analysis unit that analyzes a pixel value of the still image and extracts still image information, a motion image analysis unit that analyzes a pixel value of the motion image and extracts motion image information, and an associating unit that associates the still image with the motion image by comparing the still image information with the motion image information.

    Medical image processing device, endoscope system, medical image processing method, and program

    公开(公告)号:US11526986B2

    公开(公告)日:2022-12-13

    申请号:US16905888

    申请日:2020-06-18

    Inventor: Shumpei Kamon

    Abstract: There are provided a medical image processing device, an endoscope system, a medical image processing method, and a program which detect an optimal lesion region according to an in-vivo position of a captured image. Images at a plurality of in-vivo positions of a subject are acquired from medical equipment that sequentially captures and displays in real time the images; positional information indicating the in-vivo position of the acquired image is acquired; from among a plurality of region-of-interest detection units that detect a region of interest from an input image and correspond to the plurality of in-vivo positions, respectively, a region-of-interest detection unit corresponding to the position indicated by the positional information is selected; and the selected region-of-interest detection unit detects a region of interest from the acquired image.

    Medical image processing system
    6.
    发明授权

    公开(公告)号:US11436726B2

    公开(公告)日:2022-09-06

    申请号:US17169525

    申请日:2021-02-07

    Inventor: Shumpei Kamon

    Abstract: A region-of-interest detection unit detects a region of interest from a medical image. A display control unit keeps displaying a detection result of the region of interest for a certain period of time on a monitor, in a case where a position of the region of interest is changed in accordance with a movement of the region of interest, at a detection position of the region of interest detected by the region-of-interest detection unit before the position of the region of interest is changed.

    Medical image learning method and medical image processing device

    公开(公告)号:US12293570B2

    公开(公告)日:2025-05-06

    申请号:US17815926

    申请日:2022-07-28

    Inventor: Shumpei Kamon

    Abstract: A trained first model is generated through first learning using a first learning image group constituted of a normal image which is a medical image having no region of interest. An input image group including at least the medical image different from the first learning image group is input to the trained first model, and abnormality detection is performed. The extracted image used for learning to prevent erroneous recognition is sorted according to a result of the abnormality detection, and second learning using a second learning image group including at least the extracted image is performed. A second model that detects the region of interest is generated through the second learning.

    Medical image processing apparatus, medical image processing method, program, and diagnosis support apparatus

    公开(公告)号:US12020808B2

    公开(公告)日:2024-06-25

    申请号:US17229966

    申请日:2021-04-14

    Inventor: Shumpei Kamon

    CPC classification number: G16H30/40 A61B6/463 G16H15/00

    Abstract: Provided are a medical image processing apparatus, a medical image processing method, a program, and a diagnosis support apparatus that appropriately control whether or not to report reporting information of a medical image independently of a user operation. The above object is achieved by a medical image processing apparatus including a reporting control unit that performs control to bring reporting information included in a medical image into either a reporting state in which the reporting information is reported by a reporting unit or a non-reporting state in which the reporting information is not reported by the reporting unit. The reporting control unit brings the reporting information into the non-reporting state in a case where the medical image satisfies a non-reporting condition and brings the reporting information into the reporting state after a non-reporting time has elapsed from when the medical image does not satisfy the non-reporting condition.

    IMAGE PROCESSING DEVICE, MEDICAL DIAGNOSIS DEVICE, ENDOSCOPE DEVICE, AND IMAGE PROCESSING METHOD

    公开(公告)号:US20230306592A1

    公开(公告)日:2023-09-28

    申请号:US18167029

    申请日:2023-02-09

    Abstract: A processor of an image processing device acquires a medical image including a lesion region, and causes a determiner to perform a determination process of determining a type of a lesion on the basis of the medical image. A first determination process is a process of determining whether the type belongs to a first group including a first type classified as a first category and a second type classified as a second category or a second group including a third type classified as the second category. A second determination process is a process of determining whether the type is the first type or the second type. The processor outputs a first signal for specifying whether the type is classified as the first category or the second category on the basis of a determination result of the first determination process and a determination result of the second determination process.

    TRAINED MODEL CONVERSION METHOD, INFERENCE METHOD, TRAINED MODEL CONVERSION APPARATUS, TRAINED MODEL, AND INFERENCE APPARATUS

    公开(公告)号:US20230230369A1

    公开(公告)日:2023-07-20

    申请号:US18188449

    申请日:2023-03-22

    Inventor: Shumpei Kamon

    CPC classification number: G06V10/82 G06N3/08 G06N3/0464 G06V10/94 G06V10/7715

    Abstract: The present invention provides a trained model conversion method, an inference method, a trained model conversion apparatus, a trained model, and an inference apparatus that are capable of reducing the cost of processing by a regularization layer. A trained model conversion method according to an aspect of the present invention includes a convolutional layer generation step of generating, for a trained convolutional neural network including at least one regularization layer, a second convolutional layer on the basis of a trained parameter of the regularization layer and a trained parameter of a first convolutional layer adjacent to the regularization layer; and a converted model generation step of replacing the regularization layer and the first convolutional layer with the second convolutional layer to generate a converted model which is a converted trained model.

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